Markov health state models are advanced decision models used to study health states that recur and change as time goes on. For example, a Markov model could be used for analyzing the progression, remission and relapse of a chronic disease and treatment outcomes over a specific period of time. These health state changes are called transitions. The decision process is not linear (as with single state decision models), so the model for an evolving disease pattern must account for the transitions. Markov models are particularly relevant for chronic conditions and diseases that have clear stages of progression and/or severity. Drawing on their breadth and depth of experience, our modeling experts can help you determine the cost and value of various treatments and outcomes for such complex changing disease states.
